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A Survey of Research Prospects for more Manageable Personal Digital Photo Collections
- 1. A Survey of Research Prospects for more Manageable Personal Digital Photo Collections Nicholas Taylor @nullhandle Personal Digital Archiving February 21, 2013 “Photo Mosaic-Orange Daisy” by Flickr user BerniMartin under CC BY-ND 2.0
- 2. what metadata matters? • event • location • people • time
- 3. the metaphorical shoebox “Shoebox No. 1” by Flickr user kahala under CC BY-NC-ND 2.0
- 4. our digital photos are proliferating Good: “How many photos have ever been taken?”
- 5. our digital photos are distributed
- 6. our digital photos are many Lev Manovich: “Cultural Analytics visualizations on ultra high resolution displays”
- 7. we have some tools “My work table and some beloved tools~ 6 of 6 photos” by Flickr user Urban Woodswalker under CC BY-NC-ND 2.0
- 8. facial recognition “Picasa Face Recognition is cool” by Flickr user vanholy under CC BY-NC-ND 2.0
- 9. geotagging flickr: “Explore everyone's photos on a Map”
- 10. social annotation “The Facebook Manual Auto-tag” by Flickr user Ambuj Saxena under CC BY 2.0
- 11. metadata standards “Adding IPTC Metadata with Adobe Bridge” by Flickr user StevenErat under CC BY-NC-SA 2.0
- 12. prospective tools “Space Cameras” by Flickr user HeatherMG under CC BY-NC-ND 2.0
- 13. tools provenance • forensics (security, law enforcement, counterterrorism) • robotics (autonomous) • consumer applications (photo management)
- 14. dating digitized photos by color processing technology Palermo, Hays, and Efros: “Dating Historical Color Images”
- 15. image orientation detection of digitized photos Datar and Qi: “Automatic Image Orientation Detection Using the Supervised Self-Organizing Map”
- 16. probabalistic regional geotagging by visual similarity Hays and Efros: “IM2GPS: estimating geographic information from a single image”
- 17. satellite and ground imagery corroboration for geotagging Grosse and Johnson: “Matching a photograph to satellite images”
- 18. identifying cities by trivial visual elements Doersch et al.: “What Makes Paris Look Like Paris?” “Eiffel Tower” by Flickr user HarshLight under CC BY 2.0
- 19. geotagging and 3D scene construction using large photo sets Snavely, Seitz, and Szeliski: “Photo Tourism: Exploring Photo Collections in 3D”
- 20. historical photo overlay WhatWasThere: “Jimmy Carter on a Campaign Stop in Plains”
- 21. automatic “event” identification by temporal and visual clustering Cooper et al.: “Temporal Event Clustering for Digital Photo Collections”
- 22. recognizing persons using body patch matching Suh and Bederson: “Semi-Automatic Photo Annotation Strategies Using Event Based Clustering and Clothing Based Person Recognition” Cooray et al.: “Identifying Person Re-Occurrences for Personal Photo Management Applications”
- 23. recognizing persons using social context Naaman et al.: “Leveraging Context to Resolve Identity in Photo Albums”
- 24. recognizing persons using social network context Stone et al.: “Autotagging Facebook: Social Network Context Improves Photo Annotation”
- 25. inferring photographer based on height of shot Farid: “Who Took That Picture? (Or at least, how tall was the photographer?)”
- 26. graphing incidence of individuals over time Hangal: “Muse: Revive Precious Memories Using Email.”
- 27. Nicholas Taylor @nullhandle “Thank You” by Flickr user muffintinmom under CC BY 2.0
Notes de l'éditeur
- https://chrome.google.com/webstore/detail/picasa/onlgmecjpnejhfeofkgbfgnmdlipdejbhttps://play.google.com/store/apps/details?id=com.amazon.clouddrive.photoshttps://www.adobe.com/designcenter-archive/photoshopelements/articles/lrvid2312_pse.htmlhttp://www.amazon.com/gp/feature.html?ie=UTF8&docId=1000828861https://www.apple.com/apps/iphoto/http://www.canon.com/camera-museum/design/frontline/product/kissx5/http://www.g4tv.com/thefeed/blog/post/725553/how-google-glass-could-change-video-games/http://www.intel.com/content/www/us/en/tablets/tablets.htmlhttp://www.wired.co.uk/news/archive/2012-10/23/memoto
- Photographic prints from different time periods have different, identifiable color signatures based on the distinct succession of chemical processing technologies, facilitating EXIF timestamp generation.
- The orientation of digitized photos can be detected and automatically corrected.
- Cities can be identified by far less than their most visually iconic landmarks. Photos from different European cities were successfully localized relying on seemingly trivial elements in the visual environment: lamp-posts, grating iron-work, door frames, etc.
- EXIF timestamps and the visual content of photos can be used to organize them into probable “events.”
- Extending recognition algorithms to examine other parts of the body aside from the face increases accuracy.
- Knowing which individuals are commonly in photos with which other individuals informs person recognition.
- Who an individual is connected to and interacts with on social networks can help narrow likely candidates for person annotation in photos.
- The angel from which the photo was shot relative to a vanishing point and the ground plane can be used to infer the height of the camera, which may provide insight into who the photographer was.
- Muse is a personal data mining application for e-mail that shows, among other things, the frequency of communications with different individuals and groups of people over time; perhaps something like it could be adapted for use with photos?